Uncertainty Analysis of Urban Sewer System using Spatial Simulation of Radar Rainfall Field: New York City Case Study

The goal of this study is to quantify the uncertainty of the treatment capacity of a city in the case of extreme rainfall events and to predict the excess runoff under the city infrastructure change. The excess runoff is directly connected with the Combined Sewer Overflow, CSO, which is crucial in urban areas from financial and environmental perspectives. A simulation based approach is employed in this study to reproduce spatially dependent extreme rainfall data. A nonparametric copula-based simulation approach is applied to the spatial fields with arbitrary dependence structures and marginal densities. The nonparametric simulator uses log-spline density estimation in the univariate setting, together with a sampling strategy to reproduce dependence across variables through a nonparametric numerical approximation of the underlying copula function. The i.i.d extreme rainfall events of the corresponding urban area - extracted from the nationwide high resolution radar data stage IV - are imported into the spatial simulator. The simulated results are used to calculate the excess runoff with respect to the Natural Resources Conservation Service approach. This framework is applied to New York City as the case study. Results confirm the significance of preserving the spatial dependence in hydrologic modelling. The excess runoff exceeds the treatment capacity of the City, with the higher probability of CSO at the eastern part of the City (Queens Borough). The excess runoff that can be retained by installing green infrastructure at the vulnerable areas of New York City is also predicted, which is beneficial for the city planners. The approach of this study is applicable to any urban area within the United States with little sewer system information.